Churn-prediction models + intervention
We build a churn-prediction model: ML that identifies who is at risk of leaving based on customer behavior, plus timely intervention scenarios (help, an offer, contact). Honestly upfront: a churn prediction is PROBABILISTIC, not exact (there are false positives and misses); a sufficient volume of quality historical data is needed (a model cannot be built on small data); a prediction is not prevention (the intervention must actually work and the reason must be fixable); the model must be monitored and updated; and it is not a guarantee of reducing churn. We make an honest early-signal tool, not 'magic against leaving'.
Churn-prediction models + intervention — overview
Churn prediction is an ML model that learns from historical data to recognize behavior patterns preceding departure (declining activity, rarer logins, dropping usage, dissatisfaction signals) and flags high-risk customers. Intervention scenarios are built on top of the model: timely help, a personal offer, CS contact, removing the cause. Honestly about probabilism, this is key: the model gives a PROBABILITY of leaving, not an exact verdict. There will be false positives (flagging a loyal customer as at-risk) and misses (not noticing a leaving one). It is a tool for prioritizing attention, not an oracle. Acting on a prediction must come with the understanding that it errs. Honestly about data, this is the foundation: the model needs a sufficient volume of quality historical data on behavior and real departures. On small data or for a new product a meaningful model cannot be built — there is nothing to learn from. We will honestly assess whether you have the data for this rather than promise ML out of nothing. Honestly about 'prediction ≠ prevention', this is critical: predicting risk is only half the job. Churn is reduced not by the prediction itself but by a WORKING intervention on a FIXABLE cause. If a customer leaves because the product does not suit them or is expensive, no timely mailing will retain them — the model only shows the risk, but the cause must be genuinely addressed. A prediction without an effective intervention is useless. Honestly about monitoring: behavior and the market change, and the model degrades — it must be monitored, retrained and quality-checked. It is a living process, not 'trained once'. Honestly about privacy: a model on behavioral data requires responsible handling and consent. Honestly about the effect: with good data and working interventions it helps react earlier and reduce preventable churn, but it is not a guarantee. Honestly about access: historical data, a resource for interventions and model maintenance are needed. An important boundary: this is prediction+intervention; win-back of those who left — 985; surveys/metrics — 996-999; pause/downgrade as interventions — 1001/1002. Picture this: instead of 'we learn about departure after the fact' — an early probabilistic signal and a working reaction, honest about the model's limits. The base price starts from 70,000 ₽ (depends on data and integration).
Problems we solve
- We learn about a customer's departure after the fact, too late to react.
- No early signal about at-risk customers.
- A prediction exists but there are no working intervention scenarios.
- Unclear whether there is enough data for a meaningful model.
What's included in the Churn-prediction models + intervention service
- A churn-prediction model on your historical data
- An assessment of data sufficiency and quality for ML
- Timely intervention scenarios for fixable causes
- Monitoring, retraining and model quality control
- Privacy and consent for behavioral data
- Honest boundaries (probabilistic not exact; prediction ≠ prevention; no guarantee)
- A link with win-back (985), pause (1001), downgrade (1002)
- Handover and review with you
What you get
- An early probabilistic signal about at-risk customers
- Working intervention scenarios for fixable causes
- A maintained model (monitoring/retraining)
- Honest boundaries (probabilistic; prediction ≠ prevention; no guarantee)
How the work goes: steps
- We assess data and build a churn-prediction model
- We design interventions for fixable causes
- We build in monitoring/retraining, honestly set boundaries with you
Why PDV Expert
- Fixed price and timeline — no surprises on the invoice.
- Report and recommendations in plain language — clear without a technical background.
- In touch at every step and answering questions about the result.
FAQ
Will the model accurately say who will leave?
No, honestly: the model gives a PROBABILITY of leaving, not an exact verdict. There will be false positives (flagging a loyal customer as at-risk) and misses (not noticing a leaving one). It is a tool for prioritizing attention, not an oracle — acting on a prediction must come with the understanding that it errs. We honestly build a probabilistic early signal rather than promise to 'accurately predict every leaver'.
Will a churn prediction reduce churn by itself?
No, honestly, and this is critical: predicting risk is half the job. Churn is reduced not by the prediction but by a WORKING intervention on a FIXABLE cause. If a customer leaves because the product does not suit them or is expensive, no timely mailing will retain them — the model only shows the risk, but the cause must be genuinely addressed. A prediction without an effective intervention is useless — we do both honestly.
Can such a model definitely be built for us?
Not necessarily, honestly: ML needs a sufficient volume of quality historical data on behavior and real departures. On small data or for a new product a meaningful model cannot be built — there is nothing to learn from. Plus the model must be monitored and retrained (behavior changes, quality degrades), a living process. We will honestly assess whether you have the data rather than promise 'AI against churn' out of nothing.
About the provider
The «Churn-prediction models + intervention» service is provided by PDV Expert — a team specialising in «Customer retention». We work under contract and deliver a written report with recommendations.